Spatial Variability and Uncertainty of Water Use Impacts from U.S. Feed and Milk Production

Integrating Dietary Impacts in Food Life Cycle Assessment

Food production and food consumption have been too long studied separately. This paper therefore reviews progresses in assessment methods and identifies how nutrition effects on human health and environmental impacts of the entire food production and consumption can and should be consistently and systematically assessed, on a life cycle-based and a health-based perspective. Main observations include: (a) The strong activity in the Life Cycle Assessment (LCA) of a large range of agriculture production, covering beyond carbon footprint the biodiversity and health impacts of land, water, fertilizers, and pesticide use. (b) The multi-functionality of all foods and the need to compare a wide range of possible alternative including comparing serving size, meal alternatives and diets. (c) The availability of epidemiological dietary risk factors expressed in DALYs, enabling the creation of an additional LCA nutritional impact category and providing much broader flexibility in the choice of the functional unit and the kind of valid comparison LCA can address. (d) The need to use Big Data and machine learning method to better understand interactions and propose healthy and sustainable food baskets. As illustrated by the fruit yogurt example, dietary impacts on human health often dominate the life cycle impacts on human health and it is strongly recommended to consider them in the life cycle inventory and impact assessment of all commodities and foods that will eventually be consumed.

Small targeted dietary changes can yield substantial gains for human health and the environment

To identify environmentally sustainable foods that promote health, we combined nutritional health-based and 18 environmental indicators to evaluate, classify and prioritize individual foods. Specifically for nutrition, we developed the Health Nutritional Index to quantify marginal health effects in minutes of healthy life gained or lost of 5,853 foods in the US diet, ranging from 74 min lost to 80 min gained per serving. Environmental impacts showed large variations and were found to be correlated with global warming, except those related to water use. Our analysis also indicated that substituting only 10% of daily caloric intake from beef and processed meat for fruits, vegetables, nuts, legumes and selected seafood could offer substantial health improvements of 48 min gained per person per day and a 33% reduction in dietary carbon footprint.

Standardized Recipes and Their Influence on the Environmental Impact Assessment of Mixed Dishes: A Case Study on Pizza

Food and diet life cycle assessment (LCA) studies offer insights on the environmental performance and improvement potential of food systems and dietary patterns. However, the influence of ingredient resolution in food-LCAs is often overlooked. To address this, four distinct decomposition methods were used to determine ingredients for mixed dishes and characterize their environmental impacts, using the carbon footprint of the U.S. daily pizza intake as a case study. Pizza-specific and daily pizza intake carbon footprints varied substantially between decomposition methods. The carbon footprint for vegetarian pizza was 0.18–0.45 kg CO2eq/serving, for meat pizza was 0.56–0.73 kg CO2eq/serving, and for currently consumed pizzas in the U.S. (26.3 g/person/day; 75 pizzas types) was 0.072–0.098 kg CO2eq/person/day. These ranges could be explained by differences in pizza coverage, ingredient resolution, availability of ingredient environmental information, and ingredient adjustability for losses between decomposition methods. From the approaches considered, the USDA National Nutrient Database for Standard Reference, which reports standardized food recipes in relative weights, appears to offer the most appropriate and useful food decompositions for food-LCAs. The influence and limitations of sources of reference flows should be better evaluated and acknowledged in food and diet LCAs.

Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production

Agriculture ranks as one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as a scientific foundation for forming effective remediation strategies. However, methods capable of accurately and efficiently calculating spatially explicit life cycle global warming (GW) and eutrophication (EU) impacts at the county scale over a geographic region are lacking. The objective of this study was to determine the most efficient and accurate model for estimating spatially explicit life cycle GW and EU impacts at the county scale, with corn production in the U.S.’s Midwest region as a case study. This study compared the predictive accuracies and efficiencies of five distinct supervised machine learning (ML) algorithms, testing various sample sizes and feature selections. The results indicated that the gradient boosting regression tree model built with approximately 4000 records of monthly weather features yielded the highest predictive accuracy with cross-validation (CV) values of 0.8 for the life cycle GW impacts. The gradient boosting regression tree model built with nearly 6000 records of monthly weather features showed the highest predictive accuracy with CV values of 0.87 for the life cycle EU impacts based on all modeling scenarios. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required the longest training time. ML algorithms demonstrated to be one million times faster than the traditional process-based model with high predictive accuracy. This indicates that ML can serve as an alternative surrogate of process-based models to estimate life-cycle environmental impacts, capturing large geographic areas and timeframes.

Spatialized freshwater ecosystem life cycle impact assessment of water consumption based on instream habitat change modeling

In this article a new characterization model and factors are proposed for the life cycle impact assessment (LCIA) of water consumption on instream freshwater ecosystems. Impact pathways of freshwater consumption leading to ecosystem damage are described and the alteration of instream physical habitat is identified as a critical midpoint for ecosystem quality. The LCIA characterization model aims to assess the change in habitat quantity due to consumptive water use. It is based on statistical, physical habitat simulation for benthic invertebrates, fish species and their size classes, and guilds of fish sharing common habitat preferences. A habitat change potential (HCP) midpoint, mechanistic indicator, is developed and computed on the French river network at the river reach scale (the river segment with variable length between the upstream and downstream nodes in the hydrographic network), for median annual discharges and dry seasons. Aggregated, multi-species HCPs at a river reach are proposed using various aggregation approaches. Subsequently, the characterization factors are spatially aggregated at watershed and sub-watershed scales. HCP is highly correlated with median and low flow discharges, which determine hydraulic characteristics of reaches. Aggregation of individual HCPs at reach scale is driven by the species most sensitive to water consumption. In spatially aggregated HCPs, consistently with their reduced smaller average discharge rate, small stream habitats determine the overall watershed characterization. The study is aimed primarily at life cycle assessment (LCA) practitioners and LCIA modelers. However, since it is the result of a productive cross-fertilization between the ecohydrology and LCA domains, it could be potentially useful for watershed management and risk assessment as well. At the moment, the proposed model is applicable in France. For a broader implementation, the development of global, high resolution river databases or the generalization of the model are needed. Our new factor represents nevertheless an advancement in freshwater ecosystems LCIA laying the basis for new metrics for biodiversity assessment.

Elevating dairy research and extension through partnership: Outcomes from the United States Department of Agriculture and National Dairy Council collaborative meeting to develop a coordination roadmap

Dairy foods provide a significant portion of the recommended daily nutrition for much of the US population. Improving the availability of safe and nutritious dairy products and decreasing the environmental impact of the dairy community continue to be high priorities for both industry and the public sector. In recognition of these shared priorities, scientists and other specialists from the USDA, National Dairy Council, industry, academia, and nongovernmental organizations participated in the "Elevating Dairy Research and Extension Through Partnership" meeting on June 19, 2018. The purpose of the meeting was to strengthen partnerships and identify dairy-related research and extension needs in human nutrition, environmental sustainability, food safety, and product innovation that would benefit from enhanced coordination and collaboration across the dairy community, academia, and government. To catalyze further progress on these topics, the meeting organizers agreed to leverage the content and expertise that emerged from the meeting to develop a dairy research and extension coordination roadmap. The roadmap will establish and articulate a vision for coordinated collaboration between USDA researchers, the National Dairy Council, university researchers, extension specialists, and other dairy community stakeholders in the private and public sectors. This article represents the proceedings of the meeting and is intended to broadly communicate the dairy research and extension discussion and next steps to the dairy research and extension communities and other stakeholders in industry, academic, and government sectors.

Exploring the relevance of spatial scale to life cycle inventory results using environmentally-extended input-output models of the United States

The accuracy of direct and indirect resource use and emissions of products as quantified in life cycle models depends in part upon the geographical and technological representativeness of the production models. Production conditions vary not just between nations, but also within national boundaries. Understanding the level of geographic resolution within large industrial nations needed to reach acceptable accuracy has not been well-tested across the broad spectrum of goods and services consumed. Using an aggregate 15-industryenvironmentally-extended input-output model of the US along with detailed interstate commodity flow data, we test the accuracy of regionalizing the national model into two-regions (state - rest of US) versus 51 regions (all US states + DC). Our findings show the two-region form predicts life cycle emissions and resources used within 10-20% of the more detailed 51-region form for most of the environmental flows studied. The two-region form is less accurate when higher variability exists in production conditions for a product.

LCIA framework and cross-cutting issues guidance within the UNEP-SETAC Life Cycle Initiative

Increasing needs for decision support and advances in scientific knowledge within life cycle assessment (LCA) led to substantial efforts to provide global guidance on environmental life cycle impact assessment (LCIA) indicators under the auspices of the UNEP-SETAC Life Cycle Initiative. As part of these efforts, a dedicated task force focused on addressing several LCIA cross-cutting issues as aspects spanning several impact categories, including spatiotemporal aspects, reference states, normalization and weighting, and uncertainty assessment. Here, findings of the cross-cutting issues task force are presented along with an update of the existing UNEP-SETAC LCIA emission-to-damage framework. Specific recommendations are provided with respect to metrics for human health (Disability Adjusted Life Years, DALY) and ecosystem quality (Potentially Disappeared Fraction of species, PDF). Additionally, we stress the importance of transparent reporting of characterization models, reference states, and assumptions, in order to facilitate cross-comparison between chosen methods and indicators. We recommend developing spatially regionalized characterization models, whenever the nature of impacts shows spatial variability and related spatial data are available. Standard formats should be used for reporting spatially differentiated models, and choices regarding spatiotemporal scales should be clearly communicated. For normalization, we recommend using external normalization references. Over the next two years, the task force will continue its effort with a focus on providing guidance for LCA practitioners on how to use the UNEP-SETAC LCIA framework as well as for method developers on how to consistently extend and further improve this framework.